利用多路径协作式反应与反思智能体增强大语言模型推理能力
Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents
AI总结：
本文提出RR-MP框架，通过多路径反应与反思智能体协同推理并整合多路径见解，在不额外训练的情况下提升大语言模型在道德、物理和数学等复杂科学推理任务中的准确率，实验证明其优于基线方法。
摘要：
智能体已通过大语言模型在科学推理任务中展现出潜力。然而，在处理复杂推理任务时，它们常常面临准确率不足和思维退化等挑战，这些问题阻碍了其性能发挥。为克服上述问题，我们提出了多路径推理的反应与反思智能体（RR-MP）框架，旨在增强大语言模型的推理能力。我们的方法通过采用多路径推理机制来提升科学推理准确率，其中每条路径由一个反应智能体和一个反思智能体组成，二者协同工作以防止单一智能体依赖所固有的思维退化。此外，RR-MP框架无需额外训练；它利用每条推理路径的多个对话实例以及一个独立的总结器来整合所有路径的见解。该设计融合了多样化视角，并增强了各路径上的推理能力。我们在涉及道德情境、大学物理和数学的任务上进行了零样本和少样本评估。实验结果表明，我们的方法优于基线方法，凸显了RR-MP框架在处理复杂科学推理任务方面的有效性和优势。
英文摘要：
Agents have demonstrated their potential in scientific reasoning tasks through large language models. However, they often face challenges such as insufficient accuracy and degeneration of thought when handling complex reasoning tasks, which impede their performance. To overcome these issues, we propose the Reactive and Reflection agents with Multi-Path Reasoning (RR-MP) Framework, aimed at enhancing the reasoning capabilities of LLMs. Our approach improves scientific reasoning accuracy by employing a multi-path reasoning mechanism where each path consists of a reactive agent and a reflection agent that collaborate to prevent degeneration of thought inherent in single-agent reliance. Additionally, the RR-MP framework does not require additional training; it utilizes multiple dialogue instances for each reasoning path and a separate summarizer to consolidate insights from all paths. This design integrates diverse perspectives and strengthens reasoning across each path. We conducted zero-shot and few-shot evaluations on tasks involving moral scenarios, college-level physics, and mathematics. Experimental results demonstrate that our method outperforms baseline approaches, highlighting the effectiveness and advantages of the RR-MP framework in managing complex scientific reasoning tasks.
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